Prof. Jean-Guy Schneider is a Professor and Associate Dean (Education) in the Faculty of IT at Monash University. He holds a PhD in Computer Science from the University of Bern, Switzerland, and has over 20 years of experience in higher education. His research focuses on reliable software technologies, including component-based systems, cloud/mobile computing, quantum software, and agile methodologies. He actively contributes to software engineering education and industry-relevant research. His key research areas include energy-efficient IoT architectures, machine learning model security (e.g., MLGuard project), and quantum computing abstractions. Recent work emphasizes optimizing workflows for energy/performance trade-offs and addressing challenges in open-source ML projects. Prof. Schneider has published extensively on topics like service virtualization, Docker container monitoring, and anomaly detection in cloud systems. He supervises PhD candidates in software engineering for machine learning and quantum computing.
Alexander Ilin is a Visiting Professor and part-time teacher in the Department of Computer Science at Aalto University, specializing in Artificial Intelligence and Machine Learning. He holds roles in both the Computer Science - Artificial Intelligence and Machine Learning (AIML) research area and the Professors of Practice group. His research focuses on Machine Learning, Reinforcement Learning, and Deep Learning applications, with contributions to areas like neural networks, generative models, and healthcare informatics. Dr. Ilin earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology in 2006. His work aligns with UN Sustainable Development Goals, particularly in advancing education and innovation. Key projects include the Finnish Center for Artificial Intelligence (FCAI) and the APPIA project on privacy-aware AI applications. His research explores cutting-edge topics such as diffusion models for dynamical systems, reinforcement learning for robotics, and self-supervised forecasting in healthcare. He has led projects like APPIA (2020–2021) and FCAI (2020–2026), securing grants from the Academy of Finland and Business Finland. His work bridges theory and practice, with applications in autonomous systems, nanotechnology, and medical diagnostics. Notable activities include visiting research at the UK Met Office Hadley Centre and presentations at top conferences like NeurIPS and AAMAS. His lab focuses on scalable AI solutions for real-world challenges, emphasizing ethical AI and privacy-preserving techniques.
Dr. Iftekhar Ahmed is an Associate Professor in the Department of Informatics at the University of California, Irvine’s Donald Bren School of Information & Computer Sciences. His research focuses on software engineering methodologies, emphasizing software testing, analysis, and accessibility. He holds a Ph.D. in Computer Science from Oregon State University (2018) and a B.S. in Computer Science and Engineering from Shahjalal University of Science and Technology, Bangladesh (2007). Education: Ph.D., Computer Science, Oregon State University, 2018 B.S., Computer Science and Engineering, Shahjalal University of Science and Technology, Bangladesh, 2007 His research integrates artificial intelligence, data mining, and empirical software engineering to improve software quality and safety. Key areas include: Automated testing frameworks for large systems Bug prediction models for code vulnerabilities Accessibility testing tools (e.g., Ma11y mutation framework) Safety-critical systems like autonomous vehicles Recent work explores AI-driven code generation, prompt engineering, and mitigating biases in software tools. His team received a $1.2M grant in 2022 to enhance accessibility testing tools. Ahmed also investigates human factors in software development, including developer mental health and tool adoption challenges. His publications address critical issues like code smells in quantum computing, commit message quality, and Jupyter notebook bug patterns. He actively participates in industry-academia collaborations, such as the 2023 Southern California Software Engineering Symposium.
Christof Ferreira Torres is an Assistant Professor at the Department of Computer Science and Engineering (DEI) at Instituto Superior Técnico (IST), University of Lisbon, and a researcher at INESC-ID in the Distributed, Parallel and Secure Systems (DPSS) group. His research focuses on program analysis, software security, and blockchain systems, particularly addressing vulnerabilities in smart contracts and decentralized finance (DeFi). He holds a Ph.D. from the University of Luxembourg and Technical University of Munich under Professors Radu State and Claudia Eckert. Education: Ph.D. in Computer Science, 2022 (University of Luxembourg & TU Munich) Postdoctoral Fellow at ETH Zurich (2022–2023) His research interests include blockchain security, MEV (Maximal Extractable Value) analysis, cross-chain interoperability, and privacy in Web3. Notable contributions include frameworks like Horus for attack detection in smart contracts and Elysium for automatic vulnerability patching. He actively participates in academic service, serving on program committees for major conferences like S&P, CCS, and USENIX. Recent work highlights cross-chain arbitrage dynamics, privacy leaks in web wallets, and centralized risks in blockchain infrastructure. His findings emphasize the need for decentralized solutions to counteract threats to blockchain liveness and finality. Key Awards: TLDR Research Fellowship (2024) Excellent Doctoral Thesis Award (2022) UBRI Impact Award (2022) He teaches courses on information security, dependable systems, and computer science foundations at IST and ETH Zurich. His work bridges theoretical computer science with practical blockchain security challenges, addressing both academic and industry needs.
Michael Pucher is a dedicated researcher affiliated with the Faculty of Computer Science at Vienna University of Technology, where he contributes to the Research Group Security and Privacy. Currently serving as a Visiting Researcher (October 2023 to February 2024), his work bridges academic research and practical security challenges in computing systems. He holds a Diplom-Ingenieur (Dipl.-Ing.) degree, equivalent to a Master of Science in Engineering, and a Bachelor of Science (BSc), reflecting a strong foundation in technical disciplines. Dr. Pucher's research spans the critical domains of computer security and privacy, with a focus on reverse engineering, obfuscation techniques, binary analysis, and the application of machine learning to security problems. His investigations delve into integrated circuit analysis, software protection mechanisms, and the development of resilient methods for identifying semantic functionality in obfuscated programs. Analysis of his 2022 publications reveals a cohesive research trajectory emphasizing innovative solutions for security challenges. Key themes include the integration of machine learning for clone detection in binaries, simulation-based approaches to counter obfuscation, and advanced image processing techniques for hardware reverse engineering, indicating a multidisciplinary approach that merges hardware and software security. Active in the academic community, Dr. Pucher has contributed through teaching engagements, peer review for conferences such as the IEEE Workshop on Offensive Technologies, and presentations at international venues including the IEEE Physical Assurance and Inspection of Electronics (PAINE) workshop.
Massimo La Morgia is an Assistant Professor at the Department of Computer Science, Sapienza University of Rome. He holds a Laurea Degree (summa cum laude) and a Ph.D. in Computer Science from Sapienza University. His research focuses on cybersecurity, blockchain ecosystems, social media analysis, and IoT systems. He has received awards including the 2017 Initio alla Ricerca and 2021 Invio alla Ricerca from Sapienza. His work bridges academia and industry through technology transfer projects in mobile technology, proximity payments, and machine learning applications. Key research areas include cryptocurrency market manipulation (e.g., pump-and-dump schemes), Telegram channel analysis (fake/clone detection), and defensive mechanisms against intellectual property theft. He has developed large-scale datasets like TGDataset for social network studies and pioneered studies on DeFi sniper bots and conspiracy channel economics. His publications span ACM Transactions, IEEE journals, and top conferences like USENIX Security and ACM SIGKDD. Beyond research, he advises on tunnel boring machine risk prediction tools and has pioneered web applications for structural engineering analysis.
Dr. Ana Oprescu is a Visiting Professor at the Informatics Institute of the University of Amsterdam. Her research focuses on the intersection of software engineering, AI, energy efficiency, and data privacy, with particular emphasis on sustainable computing practices. University of Amsterdam, Faculty of Science Key Research Areas: Green software engineering for AI systems, energy-efficient code generation using Large Language Models, privacy-preserving machine learning techniques, and sustainable data processing methods. She actively explores trade-offs between energy consumption, data privacy, and algorithmic accuracy. Her recent publications demonstrate a strong focus on environmentally sustainable computing, with articles covering quantisation effects on AI energy consumption, k-anonymisation impacts on machine learning, and dynamic federated learning approaches. She has also contributed to educational initiatives in green software practices. Scientific Recognition: Recipient of VENI-2014 research grant Dr. Oprescu works at the intersection of software optimization, security, and sustainability, with a particular interest in microservice architectures, model-based testing, and energy-aware system design. She has published extensively on topics like energy-driven software engineering, code clone refactoring, and distributed tracing.
Arjun Raj is a Professor of Bioengineering in the School of Engineering and Applied Sciences and Professor of Genetics in the Perelman School of Medicine at the University of Pennsylvania. He holds the Richard K. Lubin Professorship and leads a research laboratory focused on quantitative molecular biology, particularly single-cell gene expression variability and its implications in cancer and development. His research interests include chromosome structure and gene expression, non-coding RNA, and global regulation of gene expression. The lab has pioneered quantitative single-molecule RNA detection techniques, such as RNA FISH, to study transcriptional bursting and cell-to-cell variability. Applications of this work span cancer biology, stem cell research, and developmental genetics. Recent publications (2023-2025) highlight a strong emphasis on spatial transcriptomics, lineage tracing, and therapy resistance in cancer. Key findings involve nuclear speckle regulation, innate immune memory, and the development of rapid diagnostic tools. The lab's interdisciplinary approach bridges fundamental mechanisms with clinical applications in melanoma and infectious diseases. Scientific Honors: Richard K. Lubin Professorship Professor Raj mentors a diverse group of students, including PhD candidates Miles Arnett and Gianna Busch, MD/PhD students Vinay Ayyappan, Ryan Boe, and Jessica Li, and undergraduate Caitlin Fagan. His lab fosters collaboration across biology, engineering, and clinical medicine to solve complex problems in cellular function. The Raj Lab, based in the Clinical Research Building, develops innovative tools like ClampFISH for amplified RNA detection and maintains an open-science ethos through shared resources. Their work on single-cell variability has led to significant insights into cancer drug resistance and developmental robustness.
John-Paul Ore is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His research bridges software engineering and field robotics, with a focus on program analysis, system testing, and high-resolution physical simulators for robotics systems, particularly those built with the Robot Operating System (ROS). He develops software engineering methods that improve the dependability of robotics systems through techniques for dimensional analysis without developer annotations, open-source tools like PHYS, and public datasets documenting dimensional inconsistencies in real-world systems. His educational background includes a Ph.D. from the University of Nebraska-Lincoln (2019) and a B.A. in Philosophy from the University of Chicago. His interdisciplinary background informs his approach to combining software engineering with robotics to address challenges in reasoning about full-system behavior across multiple layers of abstraction. Ore's research interests center on applying program analysis techniques to software that controls robots and interacts with the physical world. His work includes abstract type inference of physical unit types (like 'meters-per-second'), probabilistic techniques for combining semantic information in identifiers with code flow inference, and empirical measurements of how developers make decisions about robotic software. He focuses on program analysis and software testing that enhances system safety and reliability while remaining practical and economically efficient. His research has significant applications in environmental monitoring, addressing climate challenges, food production, and liberating people from dangerous, dirty, and dull work. His publication record shows a clear trajectory from foundational work on dimensional analysis in robotics software to increasingly complex applications in environmental monitoring and autonomous systems. The most recent publications demonstrate expansion into Large Language Models for code analysis while maintaining focus on practical robotics applications. His research consistently addresses the critical gap between theoretical program analysis and practical robotics system development. Best Tool Demonstration Award, ISSTA'17 for Phriky-Units ACM SIGSOFT Travel Award ($300) Othmer Fellowship 2014-2018 ($8K/year) UNL CSE Outstanding Master's Thesis Award 2015 RSS 2013 Travel Grant ($500) Ore actively mentors students, having served as research mentor for undergraduates Becca Horzewski (2016-17) and Lambros Karkazis (2018). His research is supported by significant grants including FARM BILL: NRI: INT ($1,018,596 from NSF), SHF: SMALL ($499,994 from NSF), and North Carolina Space Grant ($5,000 from NASA). He is currently recruiting PhD students for his lab focused on robotics and software engineering. His laboratory work combines robotics, software engineering, and environmental monitoring, with projects including autonomous aerial water sampling systems, UAV-based environmental sensing, and tools for improving robotics software reliability. His team develops both theoretical approaches and practical implementations, often creating open-source tools that bridge the gap between academic research and industry applications.
Shichao Liu serves as an Adjunct Professor in the Surgery Department with Urology Division specialization, though his research focuses predominantly on fundamental developmental biology using porcine models. Holding both PhD and BS degrees from Northeast Agricultural University, his academic career bridges veterinary science and embryology despite the clinical departmental affiliation. PhD in Veterinary Science, Northeast Agricultural University BS in Veterinary Science, Northeast Agricultural University Dr. Liu's research centers on mammalian embryogenesis , particularly porcine early development and stem cell pluripotency . His work investigates transcription factor networks (CDX2/OCT4/SOX2), epigenetic regulation through non-coding RNAs, and species-specific trophectoderm lineage specification. Key methodologies include transcriptome analysis, gene manipulation in embryos, and cloning technologies, with significant contributions to understanding non-rodent embryonic models. Analysis of his 15 most recent publications (2014-2021) reveals three dominant research trajectories: (1) Transcriptional regulation of embryonic lineage commitment, (2) Epigenetic mechanisms in intergenerational inheritance via small RNAs, and (3) Technical optimization of porcine embryo culture and cloning. These studies consistently employ pig models to address fundamental questions in developmental biology that have implications for both agricultural biotechnology and comparative embryology.
William Edward Hahn is an Associate Professor in the Department of Mathematics and Statistics at Florida Atlantic University (FAU), where he co-directs the Machine Perception and Cognitive Robotics Laboratory (MPCR) and the FAU AI Sandbox. His research bridges mathematical theory with practical AI applications across diverse domains including finance, healthcare, and robotics. Dr. Hahn's academic foundation: Ph.D. in Complex Systems, Florida Atlantic University (2016) B.S. in Physics and Mathematics, Guilford College (2008) His core research integrates: Compressed Sensing & Sparse Modeling : Developing efficient signal reconstruction algorithms with applications in medical imaging and data analysis. Deep Learning & Machine Learning : Creating neural network architectures for financial forecasting, drug discovery, and autonomous systems. Computer Vision & Computational Neuroscience : Modeling human perception through gait analysis and biomimetic systems. Analysis of his 2018-2022 publications reveals a strategic evolution from theoretical sparse coding to applied deep learning. Key trends include bio-inspired modular architectures for general learning, transformer networks for molecular binding prediction, and GANs for robotic telesurgery. His work consistently addresses real-world challenges in substance abuse monitoring, financial markets, and medical robotics through interdisciplinary approaches. As co-director of the MPCR Lab and FAU AI Sandbox, Dr. Hahn leads initiatives that merge cognitive science with machine perception, providing critical infrastructure for AI experimentation and education while advancing the frontiers of human-robot interaction and computational neuroscience.
Safwat Hassan is an Assistant Professor at the University of Toronto's Faculty of Information . He holds a PhD from Queen’s University and has over a decade of industry experience in software engineering roles at companies like the Egyptian Space Agency, Hewlett Packard, Vodafone, and Etisalat. His research focuses on analyzing mobile apps, user-developer interactions, and improving app store quality through techniques like review analysis and release engineering. Education: PhD (Software Analysis and Intelligence Lab, Queen’s University), MSc & BSc (Helwan University). Certifications include Sun Certified Java Programmer and OMG-Certified UML Professional. Research interests span Artificial Intelligence , Mobile Computing , Software Engineering , and User-Centric Design . Recent work explores Large Language Model (LLM) applications in mobile apps, performance bug prediction, and competitive analysis of app features. Current teaching includes INF1341H: System Analysis and Process Innovation. Supervises graduate students Sara Ibrahim Al Hajj Ibrahim and Buthayna AlMulla. Active in open-source Android analysis and CI/CD configuration studies.
Önder Babur is a University Researcher at Eindhoven University of Technology in the Department of Mathematics and Computer Science, specializing in Software Engineering and Technology. His research focuses on software analytics, model-driven engineering, and machine learning applications in software development. Research interests span clone detection, analytical modeling, and information retrieval systems. His work integrates deep learning techniques to advance software development processes and source code analysis. Publications demonstrate strong focus on empirical software engineering, with recent trends showing applications in energy systems, digital twins, and cyber-physical systems. Research consistently incorporates machine learning methodologies across domains. Research Output (2023-2025): 8 journal articles on software analytics and ML applications 7 conference contributions on model-driven engineering 3 datasets related to computational modeling
Mahdi Saeedi Nikoo is a researcher affiliated with the Eindhoven University of Technology under the Mathematics and Computer Science school in the Software Engineering and Technology department. His research focuses on Software Engineering , Business Process Modeling , Service Composition , Clone Detection , and Industrial Internet of Things . The titles of his recent publications reflect a strong emphasis on empirical studies in software engineering, domain-specific languages, and architectural frameworks for IoT. Key trends in his work include the application of variability modeling in software systems, integration of cloud platforms, and analysis of open-source repositories like GitHub. Mahdi's contributions span datasets, conference papers, and journal articles, with citations in areas such as Industrial Internet of Things and Service Composition . He has no listed scientific awards, and his supervised work includes datasets and collaborative research outputs.
Jessie Galasso-Carbonnel is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. Her research focuses on software reverse engineering, knowledge extraction, variability management, and software repository mining. She holds a Ph.D. in Computer Science from the University of Montpellier (2018) and completed postdoctoral research at the French National Institute for Sustainable Development (IRD) and the University of Montréal's Geodes group. She is currently co-chairing several international conferences and workshops, including the Poster Track at MODELS’24 and the Doctoral Symposium at SPLC’24. Her academic background includes: Ph.D. in Computer Science, University of Montpellier (2018) Postdoctoral Researcher at IRD (2019–2020) and University of Montréal (2020–2023) Recipient of IVADO Postdoctoral Research Funding (2020) Research interests span: Software product line engineering Formal concept analysis AI-driven code recommendation systems Domain-specific language design Reproducible software development practices Recent contributions include advancements in: Automated repair of semantic errors in model transformations Social diversity metrics for collaborative problem-solving Low-code development frameworks for AI pipelines She actively contributes to the academic community through roles such as PC member for SANER’25 and co-chair of multiple international workshops. Her work intersects software engineering, AI, and formal methods to advance scalable and accessible software development practices.
